Abstract: Mine sudden water is one of the most threatening geological hazards during mining production, so rapid and effective identification of sudden water sources is the key to prevent mine water damage. In this study, we analyzed the water chemistry of Panji Coal Mine aquifer and carried out the testing and analysis of strontium isotopes of water in the adjacent water rock, selected seven discriminatory indexes: ⁸⁷Sr/⁸⁶Sr, Ca²⁺, Na⁺+K⁺, Mg²⁺, HCO₃⁻, SO₄²⁻, Cl⁻, combined with principal component analysis and Fisher’s theory, principal component analysis and distance discriminatory theory, principal component analysis and BP Neural network, to establish the discriminatory models of mixed water sources based on strontium isotopes (Sr-F model, Sr-D model, Sr-B model), and use the models to discriminate unknown water samples. The results show that the Sr-B model based on strontium isotopes has the best discriminative effect, and its accuracy reaches 95%. Therefore, the identification model of water inrush sources based on principal component analysis and BP neural network can effectively improve the identification accuracy of water inrush sources, accurately identify water inrush sources in adjacent limestone aquifers, and provide guarantee for mine safety production.